Diffusion Bridge Learning Between Overfitted and Underfitted Representations for speech emotion recognition

TL;DR — Diffusion Bridge Learning is a representation-space adaptation framework that connects early-stage (underfitted) and late-stage (overfitted) encoder representations via a conditional denoising diffusion process, achieving up to +6.42 percentage points in cross-lingual speech emotion recognition weighted average recall.

Key contributions

• Identifies and leverages training-stage representation complementarity in SER, balancing early-stage smoothness/robustness with late-stage discriminability.
• Proposes Diffusion Bridge Learning, a prototype-guided conditional diffusion framework learning bidirectional sample-prototype transformations.
• Incorporates paired alignment, cycle-consistency constraints, and logit-pair KL regularization to stabilize stochastic geometry reshaping.
• Demonstrates consistent cross-lingual generalization gains between English and Japanese corpora.

Problem

Speech emotion recognition encoders frequently overfit to dataset-specific acoustic properties, speaker traits, or recording artifacts, resulting in poor out-of-domain and cross-lingual generalization. While early-stage representations offer generic smoothness and late-stage representations provide class separation, existing models typically rely on a single checkpoint or training regime, forcing an unhelpful trade-off between robustness and discriminability. This lack of robust representation dynamics causes severe performance drops under domain shifts.

Method

The framework extracts paired representations from late-stage overfitted (domain A) and early-stage underfitted (domain B) checkpoints of a HuBERT Large backbone. To avoid unstable direct translation, it uses a prototype-mediated design: class prototypes serve as anchor points representing empirical class means within each domain. A lightweight conditional MLP acts as a shared denoiser predicting Gaussian noise, conditioned on domain and role identifiers via four discrete condition IDs.

The training objective combines an epsilon-prediction loss across samples and prototypes, direct sample-prototype bridge alignment losses, cycle-consistency constraints in representation space, standard cross-entropy on a shared emotion classifier (applied to both endpoints and translations), and a logit-pair Kullback-Leibler (KL) regularization matching softened class-posterior distributions between endpoints and translations.

At inference time, original and sample-to-prototype translated representations are evaluated using the shared classifier, reshaping over-specialized directions into a class-centered geometry.

Experimental setup

Experiments use three emotional speech corpora: IEMOCAP (5,531 utterances, English), MSP-IMPROV (7,798 utterances, English), and JTES (20,000 utterances, Japanese). Cross-lingual evaluation is conducted in two directions: English-to-Japanese (train on IEMOCAP + MSP-IMPROV, test on JTES) and Japanese-to-English (train on JTES, test on IEMOCAP + MSP-IMPROV). Performance is measured via Weighted Average Recall (WAR) in percentage points across five random seeds, using a HuBERT Large backbone following the SUPERB configuration.

Results

In English-to-Japanese evaluation, the high-weight setting (lambda_pair=10, lambda_cycle=1) for the overfit-to-underfit condition yields an average improvement of +6.42% WAR (p = 0.0011, max +8.10%), outperforming low-weight (+4.42%) and unconstrained settings (+3.70%). In Japanese-to-English evaluation, the same configuration achieves a +4.00% WAR gain (p = 0.0015, max +5.20%). Same-stage bridging (underfit-underfit or overfit-overfit) yields smaller, less consistent gains, indicating that bridging heterogeneous representations is essential.

Limitations

Evaluated exclusively on cross-lingual transfer between English and Japanese, leaving multi-language or multi-domain scalability across broader linguistic families unverified. The framework relies on discrete checkpoint pairings and specific hyperparameter saturation points (lambda values around 10, 1), and overfit-side decision geometry remains comparatively rigid and resistant to stochastic smoothing.

Why read this

Speech and ML researchers focusing on out-of-distribution generalization or representation engineering will find a novel formulation of diffusion models as representation-space bridges rather than generative data pipelines. It offers a clear blueprint for combining early- and late-training dynamics to improve robustness.

Code

None released (as of this page's updated date). If you are an author with a repo, please claim this entry — see CONTRIBUTING.md.

Applications

Cross-lingual speech emotion recognition, empathic conversational agents, and robust paralinguistic analysis systems operating under domain shifts.

Institutions

National Institute of Advanced Industrial Science and Technology

Related

• Learning Emotion-discriminative Representations for Zero-Shot Cross-Lingual Speech Emotion Recognition — same problem · relatedness 2.6/3
• Multi-Loss Learning for Speech Emotion Recognition with Energy-Adaptive Mixup and Frame-Level Attention — same problem · relatedness 2.5/3
• Segment-wise Embedding based Graph Attention Network for Effective Speech Emotion Recognition — same problem · relatedness 2.4/3
• TIMBRE: Layer-Wise Cross-Lingual Speech Emotion Recognition Across 49 Layers and 26 Corpora — same problem · relatedness 2.4/3
• Geometric Second-Order Feature Correlation Learning for Self-Supervised Speech Emotion Recognition — same problem · relatedness 2.3/3

All 950k paper pairs scored by TypeSafe Jev (scripts/related/); relatedness 0 = unrelated … 3 = directly comparable.